Seismic fragility curves for mid-rise reinforced concrete framed structures with different lateral loads resisting systems

The current study presents lateral load analysis of mid-rise reinforced concrete framed structures with two different lateral load resisting systems; shear walls and rigid marginal beams. The main objective here is to investigate the influence of the location of the system in the structure; i.e. arrangement of shear walls and level of the marginal beam. For that purpose, seismic fragility curves are used as an assessment tool for comparing the seismic performance of the studied structures in different situations. Incremental dynamic analysis was performed under ten ground motions to determine the yielding and collapse capacity of each building. Five performance levels were considered in the analysis. These performance levels are (i) operational, (ii) immediate occupancy, (iii) damage control, (iv) life safety and (v) collapse prevention. Fragility curves were developed for the structural models of the studied structures considering the previously mentioned performance levels. It was observed that arrangement of shear walls on the long direction of the structure has insignificant effects on its performance while interior shear walls provide the best behavior of the structure compared to exterior shear walls only and distributing shear walls internally and externally. The analysis outcomes also indicated that the presence of the rigid marginal beam in the lower storey gives more efficiency regarding to lateral loads resistance in the studied structure

Molecular identification of adenoviruses associated with respiratory infection in Egypt from 2003 to 2010.

BACKGROUND: Human adenoviruses of species B, C, and E (HAdV-B, -C, -E) are frequent causative agents of acute respiratory infections worldwide. As part of a surveillance program aimed at identifying the etiology of influenza-like illness (ILI) in Egypt, we characterized 105 adenovirus isolates from clinical samples collected between 2003 and 2010. METHODS: Identification of the isolates as HAdV was accomplished by an immunofluorescence assay (IFA) and confirmed by a set of species and type specific polymerase chain reactions (PCR). RESULTS: Of the 105 isolates, 42% were identified as belonging to HAdV-B, 60% as HAdV-C, and 1% as HAdV-E. We identified a total of six co-infections by PCR, of which five were HAdV-B/HAdV-C co-infections, and one was a co-infection of two HAdV-C types: HAdV-5/HAdV-6. Molecular typing by PCR enabled the identification of eight genotypes of human adenoviruses; HAdV-3 (n = 22), HAdV-7 (n = 14), HAdV-11 (n = 8), HAdV-1 (n = 22), HAdV-2 (20), HAdV-5 (n = 15), HAdV-6 (n = 3) and HAdV-4 (n = 1). The most abundant species in the characterized collection of isolates was HAdV-C, which is concordant with existing data for worldwide epidemiology of HAdV respiratory infections. CONCLUSIONS: We identified three species, HAdV-B, -C and -E, among patients with ILI over the course of 7 years in Egypt, with at least eight diverse types circulating

Adapting an integrated acute respiratory infections sentinel surveillance to the COVD-19 pandemic requirements, Egypt, 2020–2022

Objectives: In Egypt, an integrated surveillance for acute respiratory infections (ARIs) was established in 2016 to identify the causes of ARIs. The surveillance system includes 19 governmental hospitals. In response to the coronavirus disease 2019 (COVID-19) pandemic, the World Health Organisation (WHO) requested surveillance adaptation to address the emerging challenges. This study aims to describe the experience in Egypt of adapting ARI surveillance to the COVID-19 pandemic. Methods: WHO case definitions were used to identify patients with ARIs. Nasopharyngeal/oropharyngeal swabs were collected for SARS-CoV-2 and influenza testing. Demographic and clinical information were obtained by interviewing patients at the hospitals. During the COVID-19 pandemic, the first two outpatients daily and every fifth admitted patient were enrolled in the study. To determine the status of ARIs in Egypt during the pandemic, patient demographic, clinical and laboratory data from 2020 to 2022 were obtained and descriptive analyses were performed. Results: Overall, 18,160 patients were enrolled in the study, including 7923 (43.6%) seen at outpatient clinics and 10,237 (56.4%) inpatients. Of the study participants, 6453 (35.5%) tested positive for ARIs, including 5620 (87.1%) for SARS-CoV-2, 781 (12.1%) for influenza and 52 (0.8%) for SARS-CoV-2/influenza coinfection. SARS-CoV-2 was the cause for 95.3% of admitted patients and 65.4% of outpatients. Influenza subtypes included A/H3 (55.7%), Influenza-B (29.1%) and H1/pdm09 (14.2%). Compared with influenza, SARS-CoV-2 tended to infect the elderly, in warm weather and in urban governorates, and resulted in more hospitalisations, longer hospital stays and higher case fatalities (16.3% vs 6.6%, p < 0.001). Conclusions: ARI surveillance in Egypt was successfully adapted to the COVID-19 pandemic and effectively described the clinical characteristics and severity of circulating viruses. Surveillance reported the re-emergence of influenza with a severe course and high fatality. Surveillance is essential for monitoring the activity of respiratory viruses with the aim of guiding clinical management, including preventative and control measures

Comparison of SARS-Cov-2 omicron variant with the previously identified SARS-Cov-2 variants in Egypt, 2020–2022: insight into SARS-Cov-2 genome evolution and its impact on epidemiology, clinical picture, disease severity, and mortality

Abstract Background The o severe acute respiratory coronavirus 2 (SARS-CoV-2) pandemic has killed millions of people and caused widespread concern around the world. Multiple genetic variants of SARS-CoV-2 have been identified as the pandemic continues. Concerns have been raised about high transmissibility and lower vaccine efficacy against omicron. There is an urgent need to better describe how omicron will impact clinical presentation and vaccine efficacy. This study aims at comparing the epidemiologic, clinical, and genomic characteristics of the omicron variant prevalent during the fifth wave with those of other VOCs between May 2020 and April 2022. Methods Epidemiological data were obtained from the National Electronic Diseases Surveillance System. Secondary data analysis was performed on all confirmed COVID-19 patients. Descriptive data analysis was performed for demographics and patient outcome and the incidence of COVID-19 was calculated as the proportion of SARS-CoV-2 confirmed patients out of the total population of Egypt. Incidence and characteristics of the omicron cohort from January- April 2022, were compared to those confirmed from May 2020-December 2021. We performed the whole-genome sequencing of SARS-CoV-2 on 1590 specimens using Illumina sequencing to describe the circulation of the virus lineages in Egypt. Results A total of 502,629 patients enrolled, including 60,665 (12.1%) reported in the fifth wave. The incidence rate of omicron was significantly lower than the mean of incidences in the previous subperiod (60.1 vs. 86.3/100,000 population, p < 0.001). Symptoms were reported less often in the omicron cohort than in patients with other variants, with omicron having a lower hospitalization rate and overall case fatality rate as well. The omicron cohort tended to stay fewer days at the hospital than did those with other variants. We analyzed sequences of 2433 (1590 in this study and 843 were obtained from GISAID platform) Egyptian SARS-CoV-2 full genomes. The first wave that occurred before the emergence of global variants of concern belonged to the B.1 clade. The second and third waves were associated with C.36. Waves 4 and 5 included B.1.617.2 and BA.1 clades, respectively. Conclusions The study indicated that Omicron-infected patients had milder symptoms and were less likely to be hospitalized; however, patients hospitalized with omicron had a more severe course and higher fatality rates than those hospitalized with other variants. Our findings demonstrate the importance of combining epidemiological data and genomic analysis to generate actionable information for public health decision-making